Sentence examples for by a nonlinear mapping from inspiring English sources

The KLRC is also developed based on the theory that samples from a specific class are known to lie on a linear subspace by a nonlinear mapping.

The recent prominent works for these strategies are proposed in [5, 6, 19, 20] where the data are first transformed by a nonlinear mapping induced by a particular kernel and then efficient linear classifiers are trained in the resulting space.

In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function.

It can transform the original data space to a feature space by a nonlinear mapping through kernel methods, where is more likely to be linearly separable than in the original data space.

Specifically, each row vector in Z i is projected from the original space ( {Re}^{p_i} ) to a high-dimensional space ℜ f by a nonlinear mapping function ( begin{array}{cc}hfill boldsymbol{varPhi} left({boldsymbol{z}}_{i,j}right):{Re}^{p_i}to {Re}^f,hfill & hfill f>{p}_ihfill end{array} ).

Throughout this paper, we denote the single valued duality mapping by and denote the set of fixed points of a nonlinear mapping by (1.1).

Throughout this paper, we denote the fixed points set of a nonlinear mapping by.

Let D ( L ) = { x ∈ X : x ‴  is absolutely continuous on  R } and let L : D ( L ) → Y be the operator defined by ( L x ) ( t ) = x ‴ ( t ), t ∈ R. Define a nonlinear mapping N Y → Y Y by ( N x ) ( t ) = f ( t, x ′ ) x ″ ( t ) + h ( t, x ) x ′ ( t ) − g ( t, x ( t ) ). (3.3).

This mapping from the input vector space to the feature space is a nonlinear mapping achieved by using kernel functions.

The self-normalization warp in the BISN context refers to a nonlinear mapping (as defined by (3) and (4)) whereas in the VTLN context the speaker normalization warp refers to a linear mapping of the frequency axis.

Let E be a real q-uniformly Banach space with its dual E*, q > 1, denote the duality between E and E* by 〈·, ·〉 and the norm of E by || · ||, and T: E → E be a nonlinear mapping.